AI Article Synopsis

  • A scientific literature review was conducted on 31 organophosphorus pesticides to gather crucial physiochemical and biological data for developing models that evaluate human risk.
  • Most of the collected data were insufficient for creating predictive QSAR models, with only parathion, chlorpyrifos, and isofenphos having comprehensive modeling information.
  • There's a need for a dedicated program to generate more extensive physicochemical and biological data to enhance risk assessment methodologies.

Article Abstract

A search of the scientific literature was carried out for physiochemical and biological data [i.e., IC50, LD50, Kp (cm/h) for percutaneous absorption, skin/water and tissue/blood partition coefficients, inhibition ki values, and metabolic parameters such as Vmax and Km] on 31 organophosphorus pesticides (OPs) to support the development of predictive quantitative structure-activity relationship (QSAR) and physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models for human risk assessment. Except for work on parathion, chlorpyrifos, and isofenphos, very few modeling data were found on the 31 OPs of interest. The available percutaneous absorption, partition coefficients and metabolic parameters were insufficient in number to develop predictive QSAR models. Metabolic kinetic parameters (Vmax, Km) varied according to enzyme source and the manner in which the enzymes were characterized. The metabolic activity of microsomes should be based on the kinetic activity of purified or cDNA-expressed cytochrome P450s (CYPs) and the specific content of each active CYP in tissue microsomes. Similar requirements are needed to assess the activity of tissue A- and B-esterases metabolizing OPs. A limited amount of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and carboxylesterase (CaE) inhibition and recovery data were found in the literature on the 31 OPs. A program is needed to require the development of physicochemical and biological data to support risk assessment methodologies involving QSAR and PBPK/PD models.

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http://dx.doi.org/10.1080/10408440490432250DOI Listing

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